Feed additives for methane mitigation: Assessment of feed additives as a strategy to mitigate enteric methane from ruminants—Accounting; How to quantify the mitigating potential of using antimethanogenic feed additives

411 ABSTRACT Recen ad ances in ou unde s anding o me hanogen- esis ha e led o he de elopmen o an ime hanogenic eed addi i es (AMFA) ha can educe en e ic me hane (CH4) emissions o a ying ex en s, ia di ec a ge ing o me hanogens, al e na i e elec on accep o s, o al e - ing he umen en i onmen . He e we examine cu en and new app oaches used o he accoun ing (i.e., quan- i ica ion) o en e ic CH4 aba emen by he use o AMFA in he li es ock sec o om he indi idual animal o he global scale. Along wi h his p ocess, ecommenda ions a e p o ided on how o accoun o he mi iga ion po- en ial a he animal le el, as well as in a m-scale mod- els, emissions ading schemes, li e cycle assessmen , and ca bon (C) oo p in ing ools, and in egional and na ional in en o ies. In addi ion, an assessmen o un- ce ain ies and po en ial ade-o s and o -se ing wi h he use o AMFA (i.e., e icacy s. e ec i eness, up- s eam and downs eam emissions) is p o ided. The ac- coun ing o on- a m en e ic CH4 emissions and bene i s om he use o AMFA s a s wi h he uminan animal (wi h es ima es ob ained om a ange o app oaches, om simple empi ical emission ac o s o equa ions o complex p ocess-based models) and goes all he way o na ional and sup ana ional accoun ing. The choice o me hodologies and le els o complexi y o accoun o mi iga ion o en e ic CH4 (o o al GHG) emissions in li es ock sys ems mus be ailo ed o he scale o analysis aimed, he a ailabili y o inpu da a o ep esen con ex- ualized condi ions, and he accoun ing objec i es (e.g., academic exe cise s. p oduce ’s GHG ce i ica ion s. na ional GHG in en o y). The accoun ing o en e ic CH4 mi iga ing e ec s needs o conside he AMFA deli e y me hods and syne gies and ade-o s o GHG emissions a le els be o e and beyond (ups eam and downs eam) he animal o ully assess he impac o AMFA use. A la ge, he accoun ing o me hane aba emen by eed ad- di i es emains o be ully assessed beyond expe imen al esul s (e icacy) o add ess p agma ism (e ec i eness), po en ial o adop ion, and socie al accep ance. Key wo ds: li e cycle assessmen , ca bon oo p in , emission ading schemes, modeling, g eenhouse gases INTRODUCTION The e is inc easing ecogni ion ha p essing ac ion mus ake place o a oid he isks and e ec s o clima e change. In his p ocess, indi iduals, o ganiza ions, and go e nmen s a e in oducing measu es o educe GHG emissions, and we need o be able o comp ehensi ely quan i y GHG emissions aba emen . En e ic me hane (CH4) emissions om li es ock sys ems mainly o igina e om mic obial e men a ion and me hanogenesis occu - ing in he o es omach o uminan s. Recen ad ances in Feed addi i es o me hane mi iga ion: Assessmen o eed addi i es as a s a egy o mi iga e en e ic me hane om uminan s—Accoun ing; How o quan i y he mi iga ing po en ial o using an ime hanogenic eed addi i es Agus in del P ado,1,2* Ronaldo E. Viba ,3* F anco M. Bilo o,4 Claudia Fa e in,5,6 Flo encia Ga cia,7 Fábio L. Hen ique,8 Fe nanda Figuei edo G anja Do ilêo Lei e,5 And e M. Mazze o,9 B adley G. Ridou ,10,11 Da id R. Yáñez-Ruiz,12 and And é Bannink13 1Basque Cen e o Clima e Change (BC3), Pa que Cien í ico de UPV/EHU, Leioa, 48940 Spain 2Ike basque—Basque Founda ion o Science, Bilbao, 48009 Spain 3AgResea ch, G asslands Resea ch Cen e, Palme s on No h 4442, New Zealand 4Depa men o Global De elopmen , College o Ag icul u e and Li e Sciences, Co nell Uni e si y, I haca, NY 14850 5Ins i u o Nacional de Tecnología Ag opecua ia (INTA), Buenos Ai es, Balca ce, 7620, A gen ina 6Uni e sidad Nacional de Ma del Pla a, Facul ad de Ciencias Exac as y Na u ales, Funes 3350, 7600, Ma del Pla a, A gen ina 7Uni e sidad Nacional de Có doba, Facul ad de Ciencias Ag opecua ias, 5000 Có doba, A gen ina 8Depa men o Biosciences, College o Ve e ina y Medicine, Uni e si y o he Republic. Mon e ideo, 11600 U uguay 9AgResea ch, Lincoln Resea ch Cen e, Lincoln 7674, New Zealand 10Commonweal h Scien i ic and Indus ial Resea ch O ganisa ion (CSIRO) Ag icul u e and Food, Clay on 3168, Vic o ia, Aus alia 11Uni e si y o he F ee S a e, Depa men o Ag icul u al Economics, Bloem on ein 9300, Sou h A ica 12Es ación Expe imen al del Zaidín, CSIC, 18008 G anada, Spain 13Wageningen Uni e si y & Resea ch, 6700 AH Wageningen, he Ne he lands J. Dai y Sci. 108:411–429 h ps://doi.o g/10.3168/jds.2024-25044 © 2025, The Au ho s. Published by Else ie Inc. on behal o he Ame ican Dai y Science Associa ion®. This is an open access a icle unde he CC BY license (h ps://c ea i ecommons.o g/licenses/by/4.0/). The lis o s anda d abb e ia ions o JDS is a ailable a adsa.o g/jds-abb e ia ions-24. Nons anda d abb e ia ions a e a ailable in he No es. Recei ed Ap il 15, 2024. Accep ed Sep embe 24, 2024. *Co esponding au ho s: agus in.delp ado@ bc3 esea ch .o g and onaldo. iba @ ag esea ch .co .nz 412 Jou nal o Dai y Science Vol. 108 No. 1, 2025 ou unde s anding o me hanogenesis ha e led o he de- elopmen o an ime hanogenic eed addi i es (AMFA) ha can educe en e ic CH4 emissions o a ying ex en s, ia di ec a ge ing o me hanogens, al e na i e elec on accep o s, o al e ing he umen en i onmen (Honan e al., 2022; Belanche e al., 2025; Du mic e al., 2025). Recen global epo s on uminan ag icul u e and clima e change ha e included he co-bene i s, isks, and implemen a ion oppo uni ies and ba ie s (IPCC, 2022), en i onmen al impac (FAO, 2020; Blonk e al., 2021), e icacy (Hega y e al., 2021; Honan e al., 2022; FAO, 2023), and acc edi ing me hodology (e.g., VERRA Ve i- ied Ca bon S anda d; VERRA, 2021) o AMFA and hei en e ic CH4 aba emen po en ial. The IPCC (2022) epo emphasizes he obus e idence and p ominen le el o consensus o p omising AMFA as e ec i e nea - e m measu es o signi ican en e ic CH4 mi iga ion. Ou o 10 AMFA o addi i e g oups assessed by Hega y e al. (2021), 2 o hem, 3-ni ooxyp opanol (3-NOP) and halogen-me hane con aining d ied Aspa agopsis sp. ( ed algae), consis en ly achie ed >20% en e ic CH4 aba e- men , ollowed by die a y ni a e (>10%), wi h o he AMFA o addi i e g oups gene ally expec ed o achie e <10% aba emen . Howe e , isks, conce ns, and unce - ain ies o using AMFA ha e been aised, such as po en- ial e ec s on pala abili y, oxici y, and animal wel a e; eeding and adminis a ion cons ain s; legal equi e- men s o au ho ize hei use (T ica ico e al., 2025); and he need o supply chains a scale and good manu ac u - ing p ac ice. In addi ion, he e is an inc easing need o adequa e accoun ing o hese aba emen s a egies. This includes he bene i s o educing en e ic CH4 (IPCC, 2022), as well as he po en ial ade-o s and syne gies in ela ion o CH4 emissions om o he p ocesses. Fo example, i AMFA supplemen a ion leads o educed eed diges ibili y, i could po en ially esul in inc eased CH4 emissions om manu e emissions. I is also impo an o conside o he sou ces o GHG, non-GHG ni ogen emissions, ups eam emissions o AMFA, and he o e all pe o mance o uminan s. This pape examines cu en and new app oaches used o he accoun ing o en e ic CH4 aba emen by he use o AMFA in he li es ock sec o om he indi idual animal o he global scale. Recommenda ions (illus a ed in Figu e 1) a e p o ided on he me hods o accoun in a m-scale models, emissions ading schemes (ETS), li e cycle assessmen (LCA; o en e e ed as ca bon oo p in when measu ing he GHG impac o a p oduc h ough e e y phase o i s li e) and in egional and na- ional in en o ies. The e m “accoun ing” he ein e e s mos ly o he quan i ica ion o en e ic CH4 aba emen a di e en scales, and o a lesse ex en , o he quan i ica- ion o unce ain ies and po en ial ade-o s o o -se ing wi h AMFA use (i.e., e icacy s. e ec i eness, o he un- ce ain ies a ec ing e icacy, ups eam and downs eam emissions). ACCOUNTING AT DIFFERENT SCALES The accoun ing o he e icacy o AMFA o mi iga e en e ic CH4 emissions in uminan li es ock sys ems in- ol es he use o gene ic es ima es, empi ical equa ions, o o he modeling app oaches (Dijks a e al., 2025) im- plemen ed in ools ha may be applied a di e en scales (animal, he d, a m, egional, and na ional; H is o e al., 2018; Tedeschi e al., 2022). The in e en ion wi h AMFA may in ol e a ious compounds wi h di e en modes o ac ion (Belanche e al., 2025) such as lipids, ionopho es, phy ochemicals, essen ial oils, algae, elec on accep o s (i.e., ni a e and sul a e), and 3-NOP o o he me hano- gen inhibi ing agen s such as b omo o m (Almeida e al., 2021; Honan e al., 2022). Feed addi i es can educe en e ic CH4 emission di ec ly, mainly ia educed CH4 p oduc ion (g ams pe day pe cow), wi h an e ec on emissions yield (g ams pe kilog am o DMI) and in en- si y (g ams pe kilog am o animal p oduc ; e.g., milk o BW gain), indi ec ly by imp o ing animal pe o mance (i.e., by al e ing he amoun s o umen e men ed OM), o bo h. Posi i e e ec s on animal pe o mance will in- cen i ize he use o AMFA when he e is no di ec ewa d o hei use o educe CH4 emissions (Dijks a e al., 2025), bu his is beyond he scope o his pape . The accoun ing o CH4 emissions aba emen a any scale ( om animal and a m-scale accoun ing o na- ional in en o y) equi es c i ical backg ound da a such as animal cha ac e is ics, dosage, and cha ac e is ics o he AMFA and he eed used as a means o deli e y, and mi iga ion e icacy and e ec i eness wi hin he a ming sys em. Such in o ma ion should be a ailable, and well- documen ed e idence is equi ed o be conside ed in any accoun ing p ocess. En e ic CH4 p edic ion models a y in le el o de ail and complexi y ep esen ed, anging om ela i ely simple empi ical (o s a is ical) models o mo e de ailed and comp ehensi e p ocess-based mecha- nis ic models ha ep esen he unde lying biological p ocesses leading o en e ic CH4 emissions (Keb eab e al., 2016; Dijks a e al., 2025). Addi ionally, C oo p in and LCA me hodology may be used o p o ide a mo e holis ic iew o he en i onmen al impac (Cowie e al., 2012) including on- a m and o - a m emissions, and ups eam and downs eam e ec s. Hence, he scale o assessmen , he me hodology used o he accoun ing o CH4 emissions (H is o e al., 2025), and he olume and quali y o da a collec ed a e in e ela ed ac o s. When he assessmen is conduc ed a a smalle scale (i.e., ani- mal, g oups o animals, he d, o a m), da a equi ed as inpu o mechanis ic models end o be collec ed mo e equen ly (o en daily o e en including diu nal aspec s) del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Jou nal o Dai y Science Vol. 108 No. 1, 2025 413 del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Figu e 1. Summa y o ecommenda ions on how o accoun o en e ic me hane (CH4) aba emen (gene al and ocusing on an ime hanogenic eed addi i e [AMFA] use) and he associa ed unce ain ies and o he non-CH4 emissions a he animal, a m, li e cycle assessmen (LCA), and coun ywide scales, as well as in emissions ading schemes (ETS). EF = emission ac o ; = unc ion o . C ea ed by A. del P ado and Sab ina Ga ay; used wi h pe mission. 414 Jou nal o Dai y Science Vol. 108 No. 1, 2025 compa ed wi h la ge scales ( egion, coun y, con inen ). In gene al, he mo e de ailed he da a equi ed and equa- ions o models applied, he g ea e he eliabili y o na ional accoun ing as a whole ( an Lingen e al., 2019). Be o e del ing in o a de ailed discussion o he a i- ous aspec s o accoun ing o en e ic CH4 emissions in AMFA, his sec ion p o ides a gene al o e iew o he me hods used a he animal, a m, and b oade scales o li es ock GHG quan i ica ion. Animal The accoun ing o on- a m en e ic CH4 emissions and bene i s om he use o AMFA s a s wi h he u- minan animal, and es ima es a e ob ained using a ange o me hods, om simple empi ical emission ac o s o equa ions o complex p ocess-based models (Figu e 2). A he animal scale, ela i ely simple es ima es o en e ic CH4 emissions a e o en ob ained om p edic ion equa- ions including DMI, ei he alone o in combina ion wi h he chemical composi ion o die cons i uen s (e.g., ibe con en ; Niu e al., 2018). Accoun ing can gain complex- i y (and o en accu acy) by adding ce ain cha ac e is ics o he animal, such as BW and animal p oduc (milk, mea , o ibe ; Keb eab e al., 2016; Dijks a e al., 2025). Emissions can also be es ima ed acco ding o a se o de ined da a, wi h es ima es o daily DMI pe animal, de i ed om abula ed ene gy equi emen s o eeding s anda ds (o en based on BW, main enance needs, is- sue g ow h, milk p oduc ion, p egnancy, and ac i i y) di ided by he ene gy concen a ion o he eed (IPCC, 2019). The diges ible ene gy (DE) alue o a eed can be es ima ed om OM diges ibili y, o om eed chemical composi ion and diges ibili y coe icien s in he li e a- u e. Feed DE can also be es ima ed by using a combina- ion o chemical composi ion da a and p edic ion equa- ions. Some mo e ad anced models p edic DE and OM and ni ogen (N) diges ibili y mechanis ically (Beukes e al., 2011; Bannink e al., 2018). An essen ial s ep o he p edic ion o a die -speci ic en e ic CH4 emission is he calcula ion o a CH4 con- e sion ac o exp essing a pe cen o eed g oss ene gy in ake (GEI) con e ed o CH4 (o en e e ed as me h- ane con e sion ac o , Ym) o a CH4 yield (CH4 p oduced pe uni o eed in ake). Me hane emission alues a e ob ained by ei he mul iplying DMI o GEI by a CH4 yield o a ixed CH4 con e sion ac o , o example, Ym (pe cen age o eed g oss ene gy con e ed o CH4) wi h alues speci ied in IPCC (2019), espec i ely. Bo h Ym and CH4 yield alues should be ob ained locally (mos likely om espi a ion chambe s) o h ough an equa ion ha migh include die a y ing edien s, chemical compo- si ion pa ame e s, diges ibili y pa ame e s, and animal cha ac e is ics. P ocess-based mechanis ic models wi h del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Figu e 2. Animal, a m, and li e cycle assessmen (LCA) bounda ies o he accoun ing o en e ic me hane (CH4) emissions. Adap ed om Cowie e al.(2012) by Sab ina Ga ay; used wi h pe mission. Jou nal o Dai y Science Vol. 108 No. 1, 2025 415 ep esen a ion o umen e men a ion and gas oin es i- nal diges ion may be used o p edic Ym alues (Bannink e al., 2011; Huh anen e al., 2015; Dijks a e al., 2025). Fa m The a m ep esen s he land scale a which manage- men decisions on li es ock p oduc ion a e made (Figu e 2). Mos emissions and a iabili y wi hin he li e cycle o ag icul u al p oduc s o en occu wi hin he a ming sys- em, ha is, wi hin he a m ga e and no du ing he es o he li e cycle o li es ock p oduc ion (Oenema e al., 2003). This is o pa icula ele ance in he case o en- e ic CH4, which is essen ially he esul o eed quali y, in ake wi hin a gi en animal ca ego y, and, in he con ex o his wo k, he use o compounds ha modula e umen e men a ion. The e o e, a m-le el models and he ac- cu a e es ima ion o en e ic CH4 emissions play a pi o al ole in add essing he mi iga ion o GHG emissions in li es ock ag icul u e. On- a m GHG models ha e been de eloped and used by he scien i ic communi y, en i- onmen al au ho i ies, a m consul an s, and a me s o he accoun ing o en e ic CH4 emissions (see examples o a m models e e enced in he ollowing pa ag aph). Those models se e se e al c ucial unc ions, includ- ing in eg al assessmen o all GHG sou ces and aising awa eness. They will also ha e o be used o iden i y, de elop, and p omo e e icacy o al e na i e AMFA and i is he e o e impo an o pinpoin knowledge gaps, as well as being able o scale up in o ma ion o policy de elopmen . Measu emen s on AMFA e icacy a e no - mally pe o med wi h indi idual animals as expe imen al uni s (H is o e al., 2025) comp ising di e en animal ca ego ies o egula o y pu poses (T ica ico e al., 2025). This basis may di e om he way an indi idual animal o animal coho s a e ep esen ed in a m-scale app oaches whe e, o ope a ional pu poses, di e en simpli ica ions and assump ions a e made. In his case, AMFA and animal coho speci ica ions and assump ions need o be well-documen ed. A he a m scale, on- a m GHG models o e a b oade di e si y o scope, modeling app oach, and scale (i.e., om he umen o he si e, he landscape, and he whole a m; Schils e al., 2007; C osson e al., 2011; Colomb e al., 2012; del P ado e al., 2013; Keb eab e al., 2016; Viba e al., 2021). E en hough models a e usually labeled as empi ical o mechanis ic, i is common o ind a combina ion o bo h app oaches wi hin a single model, each applied o di e en componen s (Dijks a e al., 2025). In gene al, a m-scale models end o ollow hyb id o empi ical app oaches o in eg a e soil, c op and pas u e, and li es ock componen s in o a a m amewo k (Schils e al., 2012). This ype o in eg a ed app oach al- lows o an o e all es ima e o di ec as well as indi ec GHG and N emissions including hei ade-o s and syne gies, and i allows compa isons be ween di e en p oduc ion sys ems, be ween di e en p oduc ion condi- ions, o bo h (Schils e al., 2007; del P ado e al., 2013; Oua aha e al., 2021). When e alua ing GHG mi iga ion s a egies om li es ock sys ems, models ha a e able o cap u e in e nal eedbacks and loops o C and N be- ween a m componen s a e p e e ed (del P ado e al., 2013) because mi iga ion measu es ha bene i one a m componen (e.g., en e ic CH4 e men a ion) may a ec C and N lows in o he componen s, o example, CH4 emissions a he manu e-managemen le el (del P ado e al., 2013). Mo eo e , modeling app oaches mus be capable o simula ing he in e ac ions be ween combined mi iga ion s a egies ha may no necessa ily be addi i e (del P ado e al., 2010; Owens e al., 2020). Fa m models can dis inguish how much o he mi iga ion comes om each s a egy h ough scena io es ing. Fi s a baseline scena io is simula ed and subsequen ly, simula ions wi h a m scena ios whe e changes a e in oduced singly and in a s epwise p ocess a e ca ied ou . Each s ep would in- oduce a new di e en change in s a egy (e.g., in ol - ing eed managemen ). The changes, ac ing singly o in combina ion, a e hen e alua ed on a ms. In addi ion o he ep esen a ion o he AMFA CH4 mi iga ing e ec , ocus is needed on he e ec o AMFA on eed diges - ibili y, exc e ion, and animal pe o mance (Belanche e al., 2025; H is o e al., 2025) and how o quan i y hese e ec s (Dijks a e al., 2025). Recommenda ions ●P ecisely de ine he aims o any a m-scale mod- eling e o and wha aspec s and de ails ha a e ele an a he animal and subanimal scale ha e and ha e no been co e ed (Dijks a e al., 2025). The ep esen a i eness o in eg a ed models o en depends on hei abili y o accu a ely depic a spe- ci ic a m wi hin a pa icula egion (i.e., he model cap u es he in icacies o local soil, clima e, a m managemen , and animal policy da a). This high- ligh s he limi a ion o a “one-size- i s-all” model o model assump ions o all a ms. O en, he lack o speci ici y and logicali y in he ep esen a ion o he unde lying p ocesses ha lead o GHG and N emissions a emp agains he in eg a ing and o e a ching app oach o modeling a he whole- a m scale because some pa s a e oo simpli ied and ep esen ed by empi ical app oaches (Oua aha e al., 2021). ●Conside es ima ing CH4 and ni ous oxide (N2O) emissions om manu e managemen , land applica- del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT 416 Jou nal o Dai y Science Vol. 108 No. 1, 2025 ion, and eces and u ine deposi ion ( esul ing om eed in ake and diges ibili y), as well as manu e ea men when e alua ing die a y e ec s. These emissions a e in luenced no only by die cha ac e - is ics bu also by bio ic and abio ic ac o s. ●Repo he a ailable da a o suppo model e alu- a ion o alida ion. Gene ally, hese da a will be limi ed o ew a m componen s. Li e Cycle Assessmen Al hough a m-le el GHG emissions balance simula- ion models all wi hin he ca ego y o sys ems analysis models and, hus, a emp o explici ly ep esen he lows and ans o ma ion o C and N, also o he app oaches a e mainly emission ac o -based and cen e a ound LCA (C osson e al., 2011; del P ado e al., 2013). Li e cycle assessmen is gene ally accep ed as a holis ic me hod o amewo k o e alua e he en i onmen al impac , such as clima e change o C oo p in as one o he indica o s, du ing he en i e li e cycle o a p oduc and ela es i o a unc ional uni exp essed in quan i a i e e ms (Guinée e al., 2002; de V ies and de Boe , 2010; Figu e 2). The LCA analysis equi es speci ic da a om he animal and a m bounda ies o achie e a mo e de ailed and speci ic assessmen (Figu e 2). The e a e 2 main ypes: a ibu- ional LCA (aLCA), which assesses he global impac sha e o a p oduc ’s li e cycle, and consequen ial LCA (cLCA), which e alua es he consequen ial impac o a decision (Schaub oeck, 2023). The C oo p in is he sum o GHG emissions associa ed wi h a p oduc o ac i i y, exp essed in uni s o ca bon dioxide equi alen s (CO2- eq; Flachowsky and Kamphues, 2012). The use o LCA o quan i y he en i onmen al impac o di e en p oduc s has inc eased in ecen yea s and is o en d i en by de- mands o accoun abili y om cus ome s, s akeholde s, and go e nmen egula o s (Beauchemin and McGeough, 2013). The In e na ional S anda ds O ganiza ion (ISO) o e s guidelines and es ablished benchma ks o he cal- cula ion and communica ion o he en i onmen al impac o ood p oduc s. Coun y Signa o y coun ies o he Pa is Ag eemen need o epo annually hei na ional emission GHG in en o y o he Uni ed Na ions F amewo k Con en ion on Clima e Change. Simul aneously, clima e ac ion plans o lowe GHG emissions h ough na ionally de e mined con ibu- ions (NDC), ha e shown ha abou 36% o coun ies included li es ock and g assland mi iga ion in e en ions in hei mos ecen NDC (C umple e al., 2021). A he na ional in en o y scale, he accoun ing o en e ic CH4 emissions can be ei he simple gene ic and easily acces- sible, be locally ob ained, o be d i en by p ocess-based mechanis ic models. The IPCC Guidelines explain he app oach o he 3 ie s o complexi y in he es ima ion o en e ic CH4 emissions om uminan li es ock sys ems (IPCC, 2006, 2019). The choice o which app oach each coun y uses o hei in en o y is based on da a a ail- abili y, esea ch o scien i ic esou ces, and mechanis ic models adap ed o he condi ions. The less ha is known abou li es ock and eed cha ac e is ics, he mo e unce - ain he in en o y is likely o be (H is o e al., 2018). In coun ies whe e an ex ensi e da abase exis s, mos ly a Tie 2 o adap ed Tie 2 app oach is ollowed, whe eas in coun ies wi h a de ailed and ex ensi e scien i ic knowledge base on diges i e and en e ic e men a i e p ocesses, a Tie 3 app oach may be used. Wi h each ie , aiming o ep esen e icacy o a AMFA linkage mus be made wi h modeling esul s a he animal scale (Dijks a e al., 2025). The la es e inemen o he IPCC Guidelines (IPCC, 2019) p oposes di e en Ym alues o hose p oposed by IPCC (2006) o ca le and bu alo (6.5% o GEI in IPCC 2006) linked o annual milk p oduc ion le els (dai y animals) and o eed quan i y and quali y. Fo example, he lowes Ym alue (5.7% o GEI) is associa ed wi h high p oducing dai y ca le ha a e ed die s wi h >70% diges ibili y and ha ha e a pe cen age o NDF in DMI <35%, whe eas he highes Ym alue (7% o GEI) mus be chosen o nondai y ca le ha g aze on low-quali y o age die s. The Tie 2 me hods can use he same app oach as Tie 1 bu wi h coun y- o egion-speci ic ene gy equi emen models o calcula e emission ac o s (i.e., al e ing Ym alues; Lassey, 2007; Hellwing e al., 2016; Colombini e al., 2023). Tie 2 me hods allow o a highe spa ial and empo al esolu ion and da a li es ock ca ego y disagg ega ion (i.e., sex, age, managemen , o season; Kouazounde e al., 2015; Ibidhi e al., 2021; Ndung’u e al., 2023). Coun ies o en lack su icien da a ela ed o li es ock o mo e beyond Tie 1. Mos GHG in en o- ies use he IPCC Tie 1 app oach, which only e lec s changes in li es ock numbe s. Moni o ing changes in managemen and p oduc i i y necessi a es using a Tie 2 app oach. The lack o ac i i y da a and incomple e o poo -quali y da a a e commonly seen as obs acles o implemen ing he Tie 2 app oach. A ecen e iew e- ealed ha ou o 140 low- and middle-income coun ies (LMIC), jus 92 ha e included li es ock- ela ed emis- sions in hei NDC (FAO and GRA, 2020). Tie 3 me hods a e o a highe o de o de ail and esolu ion, and ailo ed o assess a he subna ional o egional scale, and may, bu do no necessa ily ha e o, in ol e p ocess-based modeling ha conside s DMI, die del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Jou nal o Dai y Science Vol. 108 No. 1, 2025 417 chemical composi ion, and eed deg ada ion and e men- a ion cha ac e is ics o p edic en e ic CH4 emissions (Viba e al., 2021). Models used in Tie 3 ep esen u- men e men a ion mechanisms, cap u ing a g ea e po - ion o he a iabili y om nu i ional and animal ac o s, wi h enhanced p ecision when using local da a om ex- pe imen s and alida ed calcula ion me hods (Bannink e al., 2011; Keb eab e al., 2016). Howe e , he necessa y da a a e no ypically ga he ed and p omp ly a ailable and mus be g ounded in p io in si u umen incuba ion s udies and na ional die componen s a is ics (Bannink e al., 2011). The e o e, in coun ies whe e no such da a a e a ailable, a Tie 1 app oach is commonly ollowed, mos ly in LMIC. Emissions T ading Schemes To assis in mee ing hei emissions-aba emen commi - men s, coun ies a e inc easingly de eloping egional and na ional ETS, a ool designed o he pu pose o mee ing domes ic and in e na ional clima e change a ge s. These ma ke -based schemes aim o c ea e economic incen i es o emissions educ ion om e ec i e p ac ices imple- men ed a he leas o e all cos possible (Cowie e al., 2012). The Regional G eenhouse Gas Ini ia i e (cap and educe emissions om he powe sec o ) and he Wes e n Clima e Ini ia i e (collabo a i e de elopmen and imple- men a ion o ETS p og ams) a e examples o egional schemes in he Uni ed S a es. Likewise, he Eu opean ETS (ope a es on cap-and- ade p inciples), Aus alia’s Ca bon Fa ming Ini ia i e (a olun a y ca bon o se s scheme) and he New Zealand ETS (all sec o s o New Zealand’s economy) a e examples o na ional and sup a- na ional schemes ha ha e di e en scopes and pu poses. App o ed me hodologies o GHG accoun ing a e essen- ial o he success o hese schemes (Cowie e al., 2012). Bu o ou knowledge, e y ew o hese schemes include li es ock ag icul u e in hei accoun ing sys ems. One o he ew li es ock ag icul u e schemes includes inco po- a ing ni a es in Aus alian bee ca le. The me hodology se s he ules o he emissions aba emen achie ed by eplacing u ea lick blocks wi h ni a e lick blocks used in pas u e-based bee ca le sys ems, wi h acc edi a ion managed by he Aus alian Ca bon C edi Uni scheme (Aus alian Go e nmen , 2023). I AMFA a e o be inco po a ed as an aba emen s a - egy in ETS and he poin o obliga ion is se a he a me le el, hen he accoun ing app oach in hese ag icul u al schemes would be simila o ha used o a m-scale accoun ing (i.e., being able o accoun a he animal scale). Bu i he poin o obliga ion is se a he (animal p oduc ) p ocessing le el, hen he accoun ing app oach would mos likely be simila o ha used o egional o na ional in en o y accoun ing. APPROACHES TO THE ACCOUNTING OF ENTERIC METHANE ABATEMENT BY AMFA Animal, Fa m, Na ional, and Sup ana ional Scales Du ing he pas 60 yea s, a wide ange o AMFA ha e been es ed and expe imen ally included in die s o dai y cows (de Onda za e al., 2023; Figu e 3A). The ype o die , AMFA deli e y (in e e y mou h ul o a TMR s. pulse- ed wi h supplemen s), and p oduc ion sys em (i.e., o age- o-concen a e a io, ibe , soluble suga s, s a ch, and p o ein con en ) will de e mine no only he e ec i e- ness o he AMFA bu also he sui abili y o such eeding egimen (Dijks a e al., 2018). Keb eab e al. (2023) in a ecen me analysis epo ed ha inc eases in NDF and c ude a concen a ions abo e he a e age in he da a- base educed e ec i eness o 3-NOP a mi iga ing CH4 p oduc ion and yield, whe eas inc eases in s a ch con en enhanced 3-NOP e ec i eness in mi iga ing CH4 yield. In die s ha a e de icien in N, he di e sion o elec on lows by ni a es in o al e na i e pa hways o H2 use in he umen (i.e., a educ ion o ammonia) can p o ide bo h an e ec i e CH4 mi iga ion al e na i e and a sub- s a e o anabolism and supply o e men able N om enhanced mic obial p o ein syn hesis (Dijks a e al., 1998; an Zijde eld e al., 2010). Al hough i has been a gued ha AMFA may be less e ec i e in uminan s ed die s ha esul in less CH4 (e.g., die s high in g ains), di- e a y ac o s did no come o wa d in a me a-analysis o obse ed a ia ion in he CH4 mi iga ing e ec o added ni a e (Dijks a e al., 2025). To no e, ex e nal elec on accep o s may also p oduce oxic end compounds (i.e., sul ides, ni a es/ni i es) a ec ing animal pe o mance (La ham e al., 2016). To accele a e he de elopmen o e ec i e CH4 mi iga- ion echnologies, he e is a p essing need o comp ehend he changes b ough in he umen by he use o AMFA and hei e ec on CH4 o ma ion (Belanche e al., 2025). When e alua ing he absolu e educ ion in GHG emis- sions om he use o a speci ic AMFA, i is c ucial o accoun o he po en ial educ ion in CH4 yield (g CH4/ kg o DMI), a me ic ha links bo h en e ic CH4 emis- sions and in ake. A u he e inemen o his me ic is o exp ess CH4 yield in e ms o CH4 p oduc ion pe uni o diges ed OM (DOM; g CH4/kg o DOM in ake) because i p o ides a ine desc ip ion o eed being e men ed and con ibu ing o he e men a ion p o ile (Beauchemin e al., 2022). Wi h an assessmen a he a m scale, a CH4 mi iga ing e ec could be ep esen ed by a de aul alue o co ec- ion (Tie 1; see p e ious sec ion). Fo example, assum- ing he same amoun s o eed a e o e ed o uminan s (i.e., die s wi h and wi hou AMFA), i would be easible o apply de aul alues a ound 30% and 10% o en e ic del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT 418 Jou nal o Dai y Science Vol. 108 No. 1, 2025 del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Figu e 3. Global da a se o en e ic me hane (CH4) mi iga ion expe imen s, including he use o an ime hanogenic eed addi i es (AMFA), in lac a ing dai y cows, conduc ed be ween 1963 and 2022 (adap ed om de Onda za e al., 2023). (A) Dis ibu ion o s udies in ol ing animals supplemen ed wi h he main AMFA ca ego ized by hei die a y composi ion (% o o age). (B) Me hane yield (g CH4/kg o DMI) om animals ha ecei ed AMFA compa ed wi h he con ol g oup. The bold line in he middle o each box plo ep esen s he median. The box i sel ep esen s he in e qua ile ange, spanning om he 25 h pe cen ile (bo om o he box) o he 75 h pe cen ile ( op o he box). Whiske s ep esen a ce ain ange beyond he IQR. The iolin plo gi es a mi o ed densi y dis ibu ion o each g oup, showing he ull dis ibu ion shape and adding dep h o he aincloud plo , which combines summa y s a is ics (box plo ) wi h da a dis ibu ion ( iolin plo and indi idual poin s). The poin and line in he cen e o each cloud ep esen s i s mean and SE. The ain ep esen s indi idual da a poin s. (C) Me hane emissions in ensi ies (g CH4/kg o milk) o AMFA ha show signi ican di e ences in CH4 yield and hei ela ionship wi h indi idual milk p oduc ion (kg o milk pe head [hd] pe day). 3-NOP = 3-ni ooxyp opanol; E. Accep o = elec on accep o ; Reg. = eg ession. C ea ed by F. Bilo o and Sab ina Ga ay; used wi h pe mission. Jou nal o Dai y Science Vol. 108 No. 1, 2025 419 CH4 yield educ ion (pe kilog am o DMI) in dai y sys- ems using 3-NOP and lipids, espec i ely (Figu e 3B). Al hough such de aul alues o CH4 educ ion by AMFA a e easy o implemen in accoun ing, he assump ion inhe en ly made is ha condi ions in p ac ice ma ch he expe imen al condi ions hese alues we e de i ed om (Dijks a e al., 2025). Because his is o en no he case, i is expec ed ha his Tie 1 le el o accoun ing o CH4 educ ion will be associa ed wi h a signi ican deg ee o unce ain y (see he “Unce ain ies” sec ion). I needs o be no ed ha a gene ic es ima e only applies o he same a e age dosing and condi ions o he empi ical da a used o de i e hese es ima es. In any case, he numbe o expe imen al ials conduc ed in a m condi ions whe e CH4 measu emen me hods do no in e up hey daily beha io and ou ine (H is o e al., 2025) and conduc ed o e longe pe iods o ime (mo e han 12 o 14 wk o a whole yea ) is expanding ( an Gas elen e al., 2024). This inc eases he con idence when ansla ing mi iga ion al- ues ob ained expe imen ally in o p ac ical a ming. As we mo e om Tie 1 o Tie 2 and Tie 3 app oach- es, obse ed a ia ion needs o be cap u ed wi h mo e de- ail and ine esolu ion. In his con ex , a mo e in-dep h examina ion o nu i ional da a is impe a i e, gi en he di e si y among li es ock sys ems. Feed in ake, eed diges ibili y, and CH4 emissions a e posi i ely co ela ed wi h animal and he d size, g ow h a e, ac i i y, and p oduc ion le el, and hese di e be ween animal ypes and eed managemen p ac ices (H is o e al., 2018). Figu e 3C po ays a dec easing end in CH4 mi iga ion po en ial o AMFA as he p oduc ion le el, ene gy, and p o ein con en o he die inc ease. Highe eed qual- i y and diges ibili y, o en associa ed wi h a educ ion in e en ion ime in he umen due o as e passage a es leading o lowe CH4 yields (Beauchemin e al., 2022), some imes esul in ela i ely modes educ ions in emis- sions when AMFA a e in oduced. Howe e , he opposi e (i.e., signi ican educ ions in en e ic CH4 om cows ed highly diges ible die s) has also been shown, as demon- s a ed o 3-NOP in a yea -long s udy ( an Gas elen e al., 2024), and no such indica ions we e seen o ni a e (Feng e al., 2020). Recommenda ions ●Inc emen ally imp o ing he esolu ion o a iables ha in luence he e icacy o AMFA in an emissions in en o y is essen ial. This will esul in a mo e p ecise and accu a e assessmen o he e ec s o AMFA in speci ic ypes o nu i ional managemen , as well as in animal and a m sys ems. ●The simples way o inco po a e he e ec o AMFA in GHG accoun ing sys ems is by using he de aul uni o pe cen educ ion o CH4 pe amoun o inges ed eed by he animal (e.g., pe kilog am o DMI). This pe cen age mus ake in o accoun he basic in e ac ions be ween eed in ake and he ype o die , as well as he mode o ac ion, deli e y me hod, and e ec i e dosage o he AMFA. ●Mo e complex app oaches can imp o e he es i- ma es o en e ic CH4 educ ion cu en ly a ailable om me a-analyses. These app oaches can also a ibu e expec a ions o a ious combina ions o AMFA and die a y s a egies. A he a m scale, del P ado e al. (2010) simula ed he inclusion o lipid-based addi i es (in isola ion and in combina ion) as one o se e al s a egies o imp o e dai y a m sus ainabili y in he Uni ed Kingdom using he whole- a m model SIMSDAIRY (del P ado e al., 2011). Fo en e ic CH4, he app oach was based on an empi i- cal equa ion ha included animal DMI and he deg ee o unsa u a ion o he a y acids in he die wi h CH4 ou pu exp essed pe kilog am o DMI (Gige -Re e din e al., 2003). O he me a-analyses ha e no es ablished a clea e ec o ype o a y acid on CH4 aba emen (e.g., G ainge and Beauchemin, 2011). Due o a ia ion in he ype o die s, AMFA mode o ac ion, and eeding managemen (con inemen eeding s. a sole o supple- men ed g azing sys em), he ex en o which CH4 aba e- men is e ec i e is ha de o cap u e, and consequen ly also empi ical equa ions can s ill be poo p edic o s o GHG emissions in a speci ic a m (Viba e al., 2021). Recommenda ions ●Include su icien de ail on li es ock and eeding condi ions, such as compa ing ba n and pas u e condi ions. ●P o ide a comp ehensi e assessmen o syne gies and ade-o s wi hin he a ming ope a ion (del P ado e al., 2013). ●Include e ec s on le el o eed in ake and animal pe o mance, allowance o me abolizable ene gy (e.g., Belanche e al., 2025; an Gas elen e al., 2024), eed diges ibili y, exc e ion o u ine and eces, as well as manu e cap u e and s o age. To ou knowledge, GHG emission in en o ies a he na ional scale a e ye o accoun o he use o AMFA. O he ypes o addi i es (e.g., o imp o e he diges ibil- i y o speci ic nu ien s, pa icula ly p o eins) di ec ly a ec ing eed u iliza ion and animal pe o mance may ha e been au oma ically inco po a ed due o he highly empi ical na u e o he ac i i y da a and nu i ional e- qui emen s. Fo example, he inclusion and accoun ing o new enzymes and syn he ic amino acids in Spanish pig die s in he 2007 o 2010 pe iod esul ed in a educ- del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT 426 Jou nal o Dai y Science Vol. 108 No. 1, 2025 REFERENCES Almeida, A. K., F. C. Cowley, and R. S. Hega y. 2023. A egional-scale assessmen o nu i ional-sys em s a egies o aba emen o en e ic me hane om g azing li es ock. Anim. P od. Sci. 63:1461–1472. h ps: / / doi .o g/ 10 .1071/ AN22315. Almeida, A. K., R. S. Hega y, and A. Cowie. 2021. Me a-analysis quan i ying he po en ial o die a y addi i es and umen modi ie s o me hane mi iga ion in uminan p oduc ion sys ems. Anim. Nu . 7:1219–1230. h ps: / / doi .o g/ 10 .1016/ j .aninu .2021 .09 .005. Al a ez-Hess, P. S., S. M. Li le, P. J. Moa e, J. L. Jacobs, K. A. Beauche- min, and R. J. Ecka d. 2019. A pa ial li e cycle assessmen o he g eenhouse gas mi iga ion po en ial o eeding 3-ni ooxyp opanol and ni a e o ca le. Ag ic. Sys . 169:14–23. h ps: / / doi .o g/ 10 .1016/ j .agsy .2018 .11 .008. A ango, J., M. Bas idas, C. Cos a J ., R. González, A. Ma in, N. Ma iz, A. Ruden, and D. Villegas. 2022. Ca bon oo p in and mi iga ion scena ios o Hacienda San Jose: Iden i ying oppo uni ies and chal- lenges using a consolida ed modelling amewo k. Final Repo . In- e na ional Cen e o T opical Ag icul u e (CIAT), Cali, Colombia. Accessed Oc . 29, 2024. h ps: / / hdl .handle .ne / 10568/ 121105. A nd , C., A. N. H is o , W. J. P ice, S. C. McClelland, A. M. Pelaez, S. F. Cue a, J. Oh, J. Dijks a, A. Bannink, A. R. Baya , L. A. C omp on, M. A. Eugene, D. Enaho o, E. Keb eab, M. K euze , M. McGee, C. Ma in, C. J. Newbold, C. K. Reynolds, A. Schwa m, K. J. Shing ield, J. B. Veneman, D. R. Yanez-Ruiz, and Z. Yu. 2022. Full adop ion o he mos e ec i e s a egies o mi iga e me hane emissions by uminan s can help mee he 1.5°C a ge by 2030 bu no 2050. P oc. Na l. Acad. Sci. USA 119:e2111294119. h ps: / / doi .o g/ 10 .1073/ pnas .2111294119. Aus alian Go e nmen . 2023. Depa men o Clima e Change, Ene gy, he En i onmen and Wa e . Reducing g eenhouse gas emissions in bee ca le h ough eeding ni a e con aining supplemen s me hod. Accessed Oc . 20, 2023. h ps: / / www .dcceew .go .au/ clima e -change/ emissions - educ ion/ emissions - educ ion - und/ me hods/ educing -g eenhouse -gas -emissions -in -bee -ca le - h ough - eeding -ni a e -con aining -supplemen s. Bannink, A., W. J. Spek, J. Dijks a, and L. B. J. Šebek. 2018. A Tie 3 me hod o en e ic me hane in dai y cows applied o ecal N diges - ibili y in he ammonia in en o y. F on . Sus ain. Food Sys . 2:66. h ps: / / doi .o g/ 10 .3389/ su s .2018 .00066. Bannink, A., M. W. an Schijndel, and J. Dijks a. 2011. A model o en e ic e men a ion in dai y cows o es ima e me hane emission o he Du ch Na ional In en o y Repo using he IPCC Tie 3 ap- p oach. Anim. Feed Sci. Technol. 166–167:603–618. h ps: / / doi .o g/ 10 .1016/ j .ani eedsci .2011 .04 .043. Beauchemin, K. A., and E. J. McGeough. 2013. Li e Cycle Assessmen in Ruminan P oduc ion. CABI Books. CABI. h ps: / / doi .o g/ 10 .1079/ 9781780640426 .0212. Beauchemin, K. A., E. M. Unge eld, A. L. Abdalla, C. Al a ez, C. A nd , P. Becque , C. Benchaa , A. Be nd , R. M. Mau icio, T. A. McAllis- e , W. Oyhan cabal, S. A. Salami, L. Shalloo, Y. Sun, J. T ica ico, A. Uwizeye, C. De Camillis, M. Be noux, T. Robinson, and E. Keb eab. 2022. In i ed e iew: Cu en en e ic me hane mi iga ion op ions. J. Dai y Sci. 105:9297–9326. h ps: / / doi .o g/ 10 .3168/ jds .2022 -22091. Belanche, A., A. Bannink, J. Dijks a, Z. Du mic, F. Ga cia, F. G. San os, S. Huws, J. Jeyana han, P. Lund, R. I. Mackie, T. A. McAllis e , D. P. Mo ga i, S. Mue zel, D. W. Pi a, D. R. Yáñez-Ruiz, and E. M. Unge eld. 2025. Feed addi i es o me hane mi iga ion: A guideline o unco e he mode o ac ion o an ime hanogenic eed addi i es o uminan s. J. Dai y Sci. 108:375–394. h ps: / / doi .o g/ 10 .3168/ jds .2024 -25046. Belanche, A., A. N. H is o , H. J. an Lingen, S. E. Denman, E. Keb eab, A. Schwa m, M. K euze , M. Niu, M. Eugène, V. Nide ko n, C. Ma - in, H. A chimède, M. McGee, C. K. Reynolds, L. A. C omp on, A. R. Baya , Z. Yu, A. Bannink, J. Dijks a, A. V. Cha es, H. Cla k, S. Mue zel, V. Lind, J. M. Moo by, J. A. Rooke, A. Aub y, W. An ezana, M. Wang, R. Hega y, V. Hu on Oddy, J. Hill, P. E. Ve coe, J. V. Sa ian, A. L. Abdalla, Y. A. Sol an, A. L. Gomes Mon ei o, J. C. Ku-Ve a, G. Jau ena, C. A. Gómez-B a o, O. L. Mayo ga, G. F. S. Congio, and D. R. Yáñez-Ruiz. 2023. P edic ion o en e ic me hane emissions by sheep using an in e con inen al da abase. J. Clean. P od. 384:135523. h ps: / / doi .o g/ 10 .1016/ j .jclep o .2022 .135523. Be ça, A. S., L. O. Tedeschi, A. da Sil a Ca doso, and R. A. Reis. 2023. Me a-analysis o he ela ionship be ween die a y condensed an- nins and me hane emissions by ca le. Anim. Feed Sci. Technol. 298:115564. h ps: / / doi .o g/ 10 .1016/ j .ani eedsci .2022 .115564. Beukes, P. C., P. G ego ini, and A. J. Rome a. 2011. Es ima ing g een- house gas emissions om New Zealand dai y sys ems using a mech- anis ic whole a m model and in en o y me hodology. Anim. Feed Sci. Technol. 166–167:708–720. h ps: / / doi .o g/ 10 .1016/ j .ani eedsci .2011 .04 .050. Blonk, H., H. Bosch, N. B aconi, S. Van Cauwenbe ghe, and B. Kok. 2021. The applicabili y o LCA guidelines o model he e ec s o eed addi i es on he en i onmen al oo p in o animal p oduc ion. Blonk Consul an s and DSM Nu i ional P oduc s. Cain, M., J. Lynch, M. R. Allen, J. S. Fugles ed , D. J. F ame, and A. H. Macey. 2019. Imp o ed calcula ion o wa ming-equi alen emis- sions o sho -li ed clima e pollu an s. NPJ Clim. A mos. Sci. 2:29. h ps: / / doi .o g/ 10 .1038/ s41612 -019 -0086 -4. Came -Pesci, B., D. W. Lai d, M. an Keulen, A. Vadi eloo, M. Chalm- e s, and N. R. Moheimani. 2023. Oppo uni ies o Aspa agopsis sp. cul i a ion o educe me hanogenesis in uminan s: A c i ical e iew. Algal Res. 76:103308. h ps: / / doi .o g/ 10 .1016/ j .algal .2023 .103308. Colomb, V., M. Be noux, L. Bockel, J.-L. Cho e, S. Ma in, C. Ma in- Phipps, J. Mousse , M. Tinlo , and O. Touchemoulin. 2012. Re iew o GHG calcula o s in ag icul u e and o es y sec o s: A guideline o app op ia e choice and use o landscape based ools. Ve sion 2.0. Agence de l'En i onnemen e de la Maî ise de l'Ene gie (ADEME, F ench Agency o En i onmen and Ene gy Managemen ), Ins i u de Reche che pou le dé eloppemen (IRD, F ench Resea ch Ins i u e o De elopmen ) and he Food and Ag icul u e O ganiza ion o he Uni ed Na ions (FAO). Accessed Oc . 29, 2024. h ps: / / www . ao .o g/ ileadmin/ empla es/ ex _ac / pd / ADEME/ Re iew _exis ingGHG ool _VF _UK4 .pd . Colombini, S., A. R. G aziosi, G. Galassi, G. Gislon, G. M. C o e o, D. En iquez-Hidalgo, and L. Rape i. 2023. E alua ion o In e go e n- men al Panel on Clima e Change (IPCC) equa ions o p edic en e ic me hane emission om lac a ing cows ed Medi e anean die s. JDS Commun. 4:181–185. h ps: / / doi .o g/ 10 .3168/ jdsc .2022 -0240. Congio, G. F. S., A. Bannink, O. L. Mayo ga Mogollón, G. Jau ena, H. Gonda, J. I. Ge e, M. E. Ce ón-Cucchi, A. O iz-Chu a, M. P. Tie i, O. He nández, P. Ricci, M. P. Julia ena, B. Lomba di, A. L. Abdalla, A. L. Abdalla-Filho, A. Be nd , P. P. A. Oli ei a, F. L. Hen ique, A. L. G. Mon ei o, L. I. Bo ges, H. M. N. Ribei o-Filho, L. G. R. Pe ei a, T. R. Tomich, M. M. Campos, F. S. Machado, M. I. Ma - condes, M. E. Z. Me cadan e, L. S. Sakamo o, L. G. Albuque que, P. C. F. Ca alho, J. Rosse o, J. V. Sa ian, P. H. M. Rod igues, F. P. Júnio , T. S. Mo ei a, R. M. Mau ício, J. P. Pacheco Rod igues, A. L. C. C. Bo ges, R. Reis e Sil a, H. F. Lage, R. A. Reis, A. C. Ruggie i, A. S. Ca doso, S. C. da Sil a, M. B. Chia ega o, S. C. Valada es- Filho, F. A. S. Sil a, D. Zane i, T. T. Be chielli, J. D. Messana, C. Muñoz, C. J. A iza-Nie o, A. M. Sie a-Ala cón, L. B. Guald ón- Dua e, L. I. Mes a-Va gas, I. C. Molina-Bo e o, R. Ba ahona- Rosales, J. A ango, X. Ga i ia-U ibe, L. A. Gi aldo Valde ama, J. R. Rose o-Nogue a, S. L. Posada-Ochoa, S. Aba ca-Monge, R. So o- Blanco, J. C. Ku-Ve a, R. Jiménez-Ocampo, E. J. Flo es-San iago, O. A. Cas elán-O ega, M. F. Vázquez-Ca illo, M. Benaouda, C. A. Gómez-B a o, V. I. A. Bolo ich, M. A. D. Céspedes, L. As iga - aga, and A. N. H is o . 2021. En e ic me hane mi iga ion s a egies o uminan li es ock sys ems in he La in Ame ica and Ca ibbean egion: A me a-analysis. J. Clean. P od. 312:127693. h ps: / / doi .o g/ 10 .1016/ j .jclep o .2021 .127693. Cos a, C. J ., E. Wollenbe g, M. Beni ez, R. Newman, N. Ga dne , and F. Bellone. 2022. Roadmap o achie ing ne -ze o emissions in global ood sys ems by 2050. Sci. Rep. 12:15064. h ps: / / doi .o g/ 10 .1038/ s41598 -022 -18601 -1. Cowie, A., R. Ecka d, and S. Eady. 2012. G eenhouse gas accoun ing o in en o y, emissions ading and li e cycle assessmen in he land- based sec o : A e iew. C op Pas u e Sci. 63:284–296. h ps: / / doi .o g/ 10 .1071/ CP11188. del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Jou nal o Dai y Science Vol. 108 No. 1, 2025 427 C osson, P., L. Shalloo, D. O’B ien, G. J. Lanigan, P. A. Foley, T. M. Boland, and D. A. Kenny. 2011. A e iew o whole a m sys ems models o g eenhouse gas emissions om bee and dai y ca le p o- duc ion sys ems. Anim. Feed Sci. Technol. 166–167:29–45. h ps: / / doi .o g/ 10 .1016/ j .ani eedsci .2011 .04 .001. C umple , K., R. Abi Khalil, E. Tanganelli, N. Rai, L. Ro edi, A. Mey- beck, V. Umulisa, J. Wol , and M. Be noux. 2021. (In e im) Global upda e epo —Ag icul u e, Fo es y and Fishe ies in he Na ion- ally De e mined Con ibu ions. En i onmen and Na u al Resou ces Managemen Wo king Pape No. 91. FAO, Rome. h ps: / / doi .o g/ 10 .4060/ cb7442en. de Onda za, M. B., A. N. H is o , and J. M. T ica ico. 2023. A global da ase o en e ic me hane mi iga ion expe imen s wi h lac a ing and non-lac a ing dai y cows conduc ed om 1963 o 2022. Da a B ie 49:109459. h ps: / / doi .o g/ 10 .1016/ j .dib .2023 .109459. de V ies, M., and I. J. M. de Boe . 2010. Compa ing en i onmen al impac s o li es ock p oduc s: A e iew o li e cycle assessmen s. Li es . Sci. 128:1–11. h ps: / / doi .o g/ 10 .1016/ j .li sci .2009 .11 .007. del P ado, A., D. Chadwick, L. Ca denas, T. Misselb ook, D. Schole ield, and P. Me ino. 2010. Explo ing sys ems esponses o mi iga ion o GHG in UK dai y a ms. Ag ic. Ecosys . En i on. 136:318–332. h ps: / / doi .o g/ 10 .1016/ j .agee .2009 .09 .015. del P ado, A., P. C osson, J. E. Olesen, and C. A. Ro z. 2013. Whole- a m models o quan i y g eenhouse gas emissions and hei po en ial use o linking clima e change mi iga ion and adap a ion in empe a e g assland uminan -based a ming sys ems. Animal 7:373–385. h ps: / / doi .o g/ 10 .1017/ S1751731113000748. del P ado, A., J. Lynch, S. Liu, B. Ridou , G. Pa do, and F. Mi loehne . 2023. Animal boa d in i ed e iew: Oppo uni ies and challenges in using GWP* o epo he impac o uminan li es ock on global empe a u e change. Animal 17:100790 h ps: / / doi .o g/ 10 .1016/ j .animal .2023 .100790. del P ado, A., P. Manzano, and G. Pa do. 2021. The ole o he Eu opean small uminan dai y sec o in s abilising global empe a u es: Les- sons om GWP* wa ming-equi alen emission me ics. J. Dai y Res. 88:8–15. h ps: / / doi .o g/ 10 .1017/ S0022029921000157. del P ado, A., T. Misselb ook, D. Chadwick, A. Hopkins, R. J. De- whu s , P. Da ison, A. Bu le , J. Sch ode , and D. Schole ield. 2011. SIMS(DAIRY): A modelling amewo k o iden i y sus ainable dai y a ms in he UK. F amewo k desc ip ion and es o o ganic sys ems and N e ilise op imisa ion. Sci. To al En i on. 409:3993–4009. h ps: / / doi .o g/ 10 .1016/ j .sci o en .2011 .05 .050. Dijks a, J., A. Bannink, G. F. S. Congio, J. L. Ellis, M. Eugène, F. Ga - cia, M. Niu, R. E. Viba , D. R. Yáñez-Ruiz, and E. Keb eab. 2025. Feed addi i es o me hane mi iga ion: Modeling he impac o eed addi i es on en e ic me hane emission o uminan s—App oaches and ecommenda ions. J. Dai y Sci. 108:356–374. h ps: / / doi .o g/ 10 .3168/ jds .2024 -25049. Dijks a, J., A. Bannink, J. F ance, E. Keb eab, and S. an Gas elen. 2018. Sho communica ion: An ime hanogenic e ec s o 3-ni o- oxyp opanol depend on supplemen a ion dose, die a y ibe con en , and ca le ype. J. Dai y Sci. 101:9041–9047. h ps: / / doi .o g/ 10 .3168/ jds .2018 -14456. Dijks a, J., J. F ance, and D. R. Da ies. 1998. Di e en ma hema ical app oaches o es ima ing mic obial p o ein supply in uminan s. J. Dai y Sci. 81:3370–3384. Du mic, Z., E. C. Duin, A. Bannink, A. Belanche, V. Ca bone, M. D. Ca o, M. C üsemann, V. Fie ez, F. Ga cia, A. H is o , M. Joch, G. Ma inez-Fe nandez, S. Mue zel, E. M. Unge eld, M. Wang, and D. R. Yáñez-Ruiz. 2025. Feed addi i es o me hane mi iga ion: Recommenda ions o iden i ica ion and selec ion o bioac i e com- pounds o de elop an ime hanogenic eed addi i es. J. Dai y Sci. 108:302–321. h ps: / / doi .o g/ 10 .3168/ jds .2024 -25045. FAO (Food and Ag icul u e O ganiza ion o he Uni ed Na ions). 2020. En i onmen al pe o mance o eed addi i es in li es ock supply chains—Guidelines o assessmen —Ve sion 1. Li es ock En i- onmen al Assessmen and Pe o mance Pa ne ship (FAO LEAP). Food and Ag icul u e O ganiza ion o he Uni ed Na ions, Rome, I aly. Accessed Oc . 29, 2024. h ps: / / openknowledge . ao .o g/ i ems/ bbe7a216 -98db -4aa9 -aa52 -b8c837be5907. FAO (Food and Ag icul u e O ganiza ion o he Uni ed Na ions). 2023. Me hane emissions in li es ock and ice sys ems—Sou ces, quan i- ica ion, mi iga ion and me ics. Food and Ag icul u e O ganiza ion o he Uni ed Na ions, Rome, I aly. Accessed Oc . 29, 2024. h ps: / / doi .o g/ 10 .4060/ cc7607en. FAO (Food and Ag icul u e O ganiza ion o he Uni ed Na ions) and GRA (Global Resea ch Alliance on Ag icul u al G eenhouse Gases). 2020. Li es ock Ac i i y Da a Guidance (L-ADG): Me hods and guidance on compila ion o ac i i y da a o Tie 2 li es ock GHG in en o ies. FAO. h ps: / / doi .o g/ 10 .4060/ ca7510en. Feng, X. Y., J. Dijks a, A. Bannink, S. an Gas elen, J. F ance, and E. Keb eab. 2020. An ime hanogenic e ec s o ni a e supplemen a ion in ca le: A me a-analysis. J. Dai y Sci. 103:11375–11385. h ps: / / doi .o g/ 10 .3168/ jds .2020 -18541. Feng, X., and E. Keb eab. 2020. Ne educ ions in g eenhouse gas emis- sions om eed addi i e use in Cali o nia dai y ca le. PLoS One 15:e0234289. h ps: / / doi .o g/ 10 .1371/ jou nal .pone .0234289. Fi kins, J. L., and K. E. Mi chell. 2023. In i ed e iew: Rumen modi- ie s in oday’s dai y a ions. J. Dai y Sci. 106:3053–3071. h ps: / / doi .o g/ 10 .3168/ jds .2022 -22644. Flachowsky, G., and J. Kamphues. 2012. Ca bon oo p in s o ood o animal o igin: Wha a e he mos p e e able c i e ia o measu e animal yields? Animals (Basel) 2:108–126. h ps: / / doi .o g/ 10 .3390/ ani2020108. Fou s, J. Q., M. C. Honan, B. M. Roque, J. M. T ica ico, and E. Keb eab. 2022. En e ic me hane mi iga ion in e en ions. T ansl. Anim. Sci. 6: xac041. h ps: / / doi .o g/ 10 .1093/ as/ xac041. Gige -Re e din, S., P. Mo and-Feh , and G. T an. 2003. Li e a u e su - ey o he in luence o die a y a composi ion on me hane p oduc- ion in dai y ca le. Li es . P od. Sci. 82:73–79. h ps: / / doi .o g/ 10 .1016/ S0301 -6226(03)00002 -2. G ainge , C., and K. A. Beauchemin. 2011. Can en e ic me hane emis- sions om uminan s be lowe ed wi hou lowe ing hei p oduc ion? Anim. Feed Sci. Technol. 166–167:308–320. h ps: / / doi .o g/ 10 .1016/ j .ani eedsci .2011 .04 .021. G andl, F., M. Fu ge , M. K euze , and M. Zehe meie . 2019. Impac o longe i y on g eenhouse gas emissions and p o i abili y o indi id- ual dai y cows analysed wi h di e en sys em bounda ies. Animal 13:198–208. h ps: / / doi .o g/ 10 .1017/ S175173111800112X. Guinée, J. B., M. Go ée, R. Heijungs, G. Huppes, R. Kleijn, A. de Kon- ing, L. an Oe s, A. Wegene Sleeswijk, S. Suh, H. A. Udo de Haes, H. de B uijn, R. an Duin, M. A. J. Huijb eg s, E. Lindeije , A. A. H. Roo da, B. L. an de Ven, and B. P. Weidema. 2002. Handbook on Li e Cycle Assessmen . Ope a ional guide o he ISO S anda ds. 1. Sp inge , Do d ech . Hega y, R. S., R. A. Co ez Passe i, K. M. Di me , Y. Wang, S. Shel- on, J. Emme -Boo h, E. Wollenbe g, T. McAllis e , S. Leahy, K. A. Beauchemin, and N. Gu wick. 2021. An e alua ion o eme ging eed addi i es o educe me hane emissions om li es ock. Edi ion 1. Repo coo dina ed by Clima e Change, Ag icul u e and Food Secu i y (CCAFS) and he New Zealand Ag icul u al G eenhouse Gas Resea ch Cen e (NZAGRC) ini ia i e o he Global Resea ch Alliance (GRA). Accessed Oc . 29, 2024. h ps: / / hdl .handle .ne / 10568/ 116489. Hellwing, A. L. F., M. R. Weisbje g, M. B ask, L. Als up, M. Johan- sen, L. Hymølle , M. K. La sen, and P. Lund. 2016. P edic ion o he me hane con e sion ac o (Ym) o dai y cows on he basis o na ional a m da a. Anim. P od. Sci. 56:535–540. h ps: / / doi .o g/ 10 .1071/ AN15520. Honan, M., X. Feng, J. M. T ica ico, and E. Keb eab. 2022. Feed addi- i es as a s a egic app oach o educe en e ic me hane p oduc ion in ca le: Modes o ac ion, e ec i eness and sa e y. Anim. P od. Sci. 62:1303–1317. h ps: / / doi .o g/ 10 .1071/ AN20295. Hö enhube , S. J., V. G ößbache , L. Schanz, and W. J. Zolli sch. 2023. Implemen ing IPCC 2019 Guidelines in o a na ional in en o y: Impac s o key changes in Aus ian ca le and pig a ming. Sus ain- abili y (Basel) 15:4814. h ps: / / doi .o g/ 10 .3390/ su15064814. H is o , A. N., A. Bannink, M. Ba elli, A. Belanche, M. C. Caja ille Sanz, G. Fe nandez-Tu en, F. Ga cia, A. Jonke , D. A. Kenny, V. Lind, S. J. Meale, D. Meo Zilio, C. Muñoz, D. Pacheco, N. Pei en, M. Ramin, L. Rape i, A. Schwa m, S. S e giadis, K. Theodo idou, del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT 428 Jou nal o Dai y Science Vol. 108 No. 1, 2025 E. M. Unge eld, S. an Gas elen, D. R. Yáñez-Ruiz, S. M. Wa e s, and P. Lund. 2025. Feed addi i es o me hane mi iga ion: Recom- menda ions o es ing en e ic me hane-mi iga ing eed addi i es in uminan s udies. J. Dai y Sci. 108:322–355. h ps: / / doi .o g/ 10 .3168/ jds .2024 -25050. H is o , A. N., E. Keb eab, M. Niu, J. Oh, A. Bannink, A. R. Baya , T. M. Boland, A. F. B i o, D. P. Caspe , L. A. C omp on, J. Dijks a, M. Eu- gene, P. C. Ga nswo hy, N. Haque, A. L. F. Hellwing, P. Huh anen, M. K euze , B. Kuhla, P. Lund, J. Madsen, C. Ma in, P. J. Moa e, S. Mue zel, C. Munoz, N. Pei en, J. M. Powell, C. K. Reynolds, A. Schwa m, K. J. Shing ield, T. M. S o lien, M. R. Weisbje g, D. R. Yanez-Ruiz, and Z. Yu. 2018. Symposium e iew: Unce ain ies in en e ic me hane in en o ies, measu emen echniques, and p edic- ion models. J. Dai y Sci. 101:6655–6674. h ps: / / doi .o g/ 10 .3168/ jds .2017 -13536. Huh anen, P., E. H. Cabezas-Ga cia, S. U sumi, and S. Zimme man. 2015. Compa ison o me hods o de e mine me hane emissions om dai y cows in a m condi ions. J. Dai y Sci. 98:3394–3409. h ps: / / doi .o g/ 10 .3168/ jds .2014 -9118. Ibidhi, R., T.-H. Kim, R. Bha anidha an, H.-J. Lee, Y.-K. Lee, N.-Y. Kim, and K.-H. Kim. 2021. De eloping coun y-speci ic me hane emis- sion ac o s and ca bon luxes om en e ic e men a ion in Sou h Ko ean dai y ca le p oduc ion. Sus ainabili y (Basel) 13:9133. h ps: / / doi .o g/ 10 .3390/ su13169133. IPCC. 2006. 2006 IPCC Guidelines o Na ional G eenhouse Gas In- en o ies. P epa ed by he Na ional G eenhouse Gas In en o ies P og amme. H. S. Eggles on, L. Buendia, K. Miwa, T. Nga a, and K. Tanabe, ed. IGES, Japan. Accessed Oc . 29, 2024. h ps: / / www .ipcc -nggip .iges .o .jp/ public/ 2006gl/ pd / 4 _Volume4/ V4 _10 _Ch10 _Li es ock .pd . IPCC. 2019. Chap e 10: Emissions om li es ock and manu e manage- men . Pages 10.1–10.207 in 2019 Re inemen o he 2006 guidelines o Na ional G eenhouse Gas In en o ies: Ag icul u e, Fo es y and O he Land Use. E. Cal o Buendia, K. Tanabe, A. K anjc, J. Baas- ansu en, M. Fukuda, S. Nga ize, A. Osako, Y. Py oshenko, P. She - manau, and S. Fede ici, ed. IPCC, Gene a, Swi ze land. Accessed Oc . 29, 2024. h ps: / / www .ipcc -nggip .iges .o .jp/ public/ 2019 / pd / 4 _Volume4/ 19R _V4 _Ch10 _Li es ock .pd . IPCC. 2022. Clima e Change 2022: Mi iga ion o Clima e Change. Con ibu ion o Wo king G oup III o he Six h Assessmen Repo o he In e go e nmen al Panel on Clima e Change. P. R. Shukla, J. Skea, R. Slade, A. Al Khou dajie, R. an Diemen, D. McCollum, M. Pa hak, S. Some, P. Vyas, R. F ade a, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, and J. Malley, ed. Camb idge Uni e si y P ess, Camb idge, UK. Keb eab, E., A. Bannink, E. M. P essman, N. Walke , A. Ka agiannis, S. an Gas elen, and J. Dijks a. 2023. A me a-analysis o e ec s o 3-ni ooxyp opanol on me hane p oduc ion, yield, and in ensi y in dai y ca le. J. Dai y Sci. 106:927–936. h ps: / / doi .o g/ 10 .3168/ jds .2022 -22211. Keb eab, E., L. Tedeschi, J. Dijks a, J. L. Ellis, A. Bannink, and J. F ance. 2016. Modeling g eenhouse gas emissions om en e ic e men a ion. Pages 173–195 in Syn hesis and Modeling o G een- house Gas Emissions and Ca bon S o age in Ag icul u al and Fo es Sys ems o Guide Mi iga ion and Adap a ion. Ame ican Socie y o Ag onomy, C op Science Socie y o Ame ica, and Soil Science Socie y o Ame ica. Klop, G., J. Dijks a, K. Dieho, W. H. Hend iks, and A. Bannink. 2017. En e ic me hane p oduc ion in lac a ing dai y cows wi h con inuous eeding o essen ial oils o o a ional eeding o essen ial oils and lau ic acid. J. Dai y Sci. 100:3563–3575. h ps: / / doi .o g/ 10 .3168/ jds .2016 -12033. Kouazounde, J. B., J. D. Gbenou, S. Baba ounde, N. S i as a a, S. H. Eggles on, C. An wi, J. Baah, and T. A. McAllis e . 2015. De elop- men o me hane emission ac o s o en e ic e men a ion in ca le om Benin using IPCC Tie 2 me hodology. Animal 9:526–533. h ps: / / doi .o g/ 10 .1017/ S1751731114002626. Lassey, K. R. 2007. Li es ock me hane emission: F om he indi idual g azing animal h ough na ional in en o ies o he global me hane cycle. Ag ic. Fo . Me eo ol. 142:120–132. h ps: / / doi .o g/ 10 .1016/ j .ag o me .2006 .03 .028. La ham, E. A., R. C. Ande son, W. E. Pinchak, and D. J. Nisbe . 2016. Insigh s on al e a ions o he umen ecosys em by ni a e and ni o- compounds. F on . Mic obiol. 7:228. h ps: / / doi .o g/ 10 .3389/ micb .2016 .00228. Maigaa d, M., M. R. Weisbje g, M. Johansen, N. Walke , C. Ohlsson, and P. Lund. 2024. E ec s o die a y a , ni a e, and 3-ni ooxyp o- panol and hei combina ions on me hane emission, eed in ake, and milk p oduc ion in dai y cows. J. Dai y Sci. 107:220–241. h ps: / / doi .o g/ 10 .3168/ jds .2023 -23420. MAPA. 2024. Minis e io de Ag icul u a, Pesca y Alimen ación. Bases zoo écnicas pa a el cálculo del balance alimen a io de ni ógeno y de ós o o en po cino blanco. 2ª Edición (in Spanish). Accessed Oc . 29, 2024. h ps: / / www .mapa .gob .es/ es/ ganade ia/ emas/ ganade ia -y -medio -ambien e/ po cino _blanco _2024 _21 -3 -24subidoaweb _ cm30 -440945 .pd . Muizelaa , W., M. G oo , G. an Duinke ken, R. Pe e s, and J. Dijks a. 2021. Sa e y and ans e s udy: T ans e o b omo o m p esen in Aspa agopsis axi o mis o milk and u ine o lac a ing dai y cows. Foods 10:584. h ps: / / doi .o g/ 10 .3390/ oods10030584. Ndung’u, P. W., C. J. L. du Toi , T. Takahashi, M. Robe son-Dean, K. Bu e bach-Bahl, L. Me bold, and J. P. Goopy. 2023. A simpli ied ap- p oach o p oducing Tie 2 en e ic-me hane emission ac o s based on Eas A ican smallholde a m da a. Anim. P od. Sci. 63:227–236. h ps: / / doi .o g/ 10 .1071/ AN22082. Nichols, K., A. Bannink, S. Pacheco, H. J. an Valenbe g, J. Dijks a, and H. an Laa . 2018. Feed and ni ogen e iciency a e a ec ed di e - en ly bu milk lac ose p oduc ion is s imula ed equally when isoene - ge ic p o ein and a is supplemen ed in lac a ing dai y cow die s. J. Dai y Sci. 101:7857–7870. h ps: / / doi .o g/ 10 .3168/ jds .2017 -14276. Nilsson, J., and M. Ma in. 2022. Explo a o y en i onmen al assessmen o la ge-scale cul i a ion o seaweed used o educe en e ic me hane emissions. Sus ain. P od. Consum. 30:413–423. h ps: / / doi .o g/ 10 .1016/ j .spc .2021 .12 .006. Niu, M., E. Keb eab, A. N. H is o , J. Oh, C. A nd , A. Bannink, A. R. Baya , A. F. B i o, T. Boland, D. Caspe , L. A. C omp on, J. Dijks a, M. A. Eugene, P. C. Ga nswo hy, M. N. Haque, A. L. F. Hellwing, P. Huh anen, M. K euze , B. Kuhla, P. Lund, J. Madsen, C. Ma in, S. C. McClelland, M. McGee, P. J. Moa e, S. Mue zel, C. Munoz, P. O’Kiely, N. Pei en, C. K. Reynolds, A. Schwa m, K. J. Shing ield, T. M. S o lien, M. R. Weisbje g, D. R. Yanez-Ruiz, and Z. Yu. 2018. P edic ion o en e ic me hane p oduc ion, yield, and in ensi y in dai y ca le using an in e con inen al da abase. Glob. Chang. Biol. 24:3368–3389. h ps: / / doi .o g/ 10 .1111/ gcb .14094. Oenema, O., H. K os, and W. de V ies. 2003. App oaches and unce ain- ies in nu ien budge s: Implica ions o nu ien managemen and en i onmen al policies. Eu . J. Ag on. 20:3–16. h ps: / / doi .o g/ 10 .1016/ S1161 -0301(03)00067 -4. Olijhoek, D. W., A. L. F. Hellwing, M. B ask, M. R. Weisbje g, O. Hojbe g, M. K. La sen, J. Dijks a, E. J. E landsen, and P. Lund. 2016. E ec o die a y ni a e le el on en e ic me hane p oduc ion, hyd ogen emission, umen e men a ion, and nu ien diges ibili y in dai y cows. J. Dai y Sci. 99:6191–6205. h ps: / / doi .o g/ 10 .3168/ jds .2015 -10691. Oua aha , L., A. Bannink, G. Lanigan, and B. Amon. 2021. Modelling he e ec o eeding managemen on g eenhouse gas and ni ogen emissions in ca le a ming sys ems. Sci. To al En i on. 776:145932. h ps: / / doi .o g/ 10 .1016/ j .sci o en .2021 .145932. Owens, J. L., B. W. Thomas, J. L. S oeckli, K. A. Beauchemin, T. A. McAl- lis e , F. J. La ney, and X. Hao. 2020. G eenhouse gas and ammonia emissions om s o ed manu e om bee ca le supplemen ed 3-ni o- oxyp opanol and monensin o educe en e ic me hane emissions. Sci. Rep. 10:19310. h ps: / / doi .o g/ 10 .1038/ s41598 -020 -75236 -w. Pe e sen, S. O., M. Blancha d, D. Chadwick, A. Del P ado, N. Edoua d, J. Mosque a, and S. G. Somme . 2013. Manu e managemen o g eenhouse gas mi iga ion. Animal 7:266–282. h ps: / / doi .o g/ 10 .1017/ S1751731113000736. P essman, E. M., S. Liu, and F. M. Mi loehne . 2023. Me hane emissions om Cali o nia dai ies es ima ed using no el clima e me ic Global del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Jou nal o Dai y Science Vol. 108 No. 1, 2025 429 Wa ming Po en ial S a show imp o ed ag eemen wi h modeled wa ming dynamics. F on . Sus ain. Food Sys . 6:1072805. h ps: / / doi .o g/ 10 .3389/ su s .2022 .1072805. Reynolds, C. K., D. J. Humph ies, P. Ki on, M. Kinde mann, S. Du al, and W. S einbe g. 2014. E ec s o 3-ni ooxyp opanol on me hane emission, diges ion, and ene gy and ni ogen balance o lac a ing dai y cows. J. Dai y Sci. 97:3777–3789. h ps: / / doi .o g/ 10 .3168/ jds .2013 -7397. Ridou , B. 2021. Clima e neu al li es ock p oduc ion—A adia i e o c- ing-based clima e oo p in app oach. J. Clean. P od. 291:125260. h ps: / / doi .o g/ 10 .1016/ j .jclep o .2020 .125260. Ridou , B., S. A. Lehne , S. Denman, E. Cha mley, R. Kinley, and S. Dominik. 2022. Po en ial GHG emission bene i s o Aspa agopsis axi o mis eed supplemen in Aus alian bee ca le eedlo s. J. Clean. P od. 337:130499. h ps: / / doi .o g/ 10 .1016/ j .jclep o .2022 .130499. Rome o-Pe ez, A., E. K. Okine, S. M. McGinn, L. L. Guan, M. Oba, S. M. Du al, M. Kinde mann, and K. A. Beauchemin. 2014. The po en- ial o 3-ni ooxyp opanol o lowe en e ic me hane emissions om bee ca le. J. Anim. Sci. 92:4682–4693. h ps: / / doi .o g/ 10 .2527/ jas .2014 -7573. Schaub oeck, T. 2023. Rele ance o a ibu ional and consequen ial li e cycle assessmen o socie y and decision suppo . F on . Sus ain. 4:1063583. h ps: / / doi .o g/ 10 .3389/ sus .2023 .1063583. Schils, R. L. M., J. L. Ellis, C. A. M. de klein, J. P. Lesschen, S. O. Pe e sen, and S. G. Somme . 2012. Mi iga ion o g eenhouse gases om ag icul u e: Role o models. Ac a Ag ic. Scand. A Anim. Sci. 62:212–224. h ps: / / doi .o g/ 10 .1080/ 09064702 .2013 .788205. Schils, R. L. M., J. E. Olesen, A. del P ado, and J. F. Soussana. 2007. A e iew o a m le el modelling app oaches o mi iga ing g een- house gas emissions om uminan li es ock sys ems. Li es . Sci. 112:240–251. h ps: / / doi .o g/ 10 .1016/ j .li sci .2007 .09 .005. Tedeschi, L. O., A. L. Abdalla, C. Al a ez, S. W. Anuga, J. A ango, K. A. Beauchemin, P. Becque , A. Be nd , R. Bu ns, C. De Camillis, J. Cha a, J. M. Echaza e a, M. Hassouna, D. Kenny, M. Ma ho , R. M. Mau icio, S. C. McClelland, M. Niu, A. A. Onyango, R. Pa ajuli, L. G. R. Pe ei a, A. Del P ado, M. Paz Tie i, A. Uwizeye, and E. Keb eab. 2022. Quan i ica ion o me hane emi ed by uminan s: A e iew o me hods. J. Anim. Sci. 100:skac197. h ps: / / doi .o g/ 10 .1093/ jas/ skac197. T ica ico, J. M., F. Ga cia, A. Bannink, S.-S. Lee, M. A. Miguel, J. R. Newbold, P. K. Rosens ein, M. R. Van de Saag, and D. R. Yáñez- Ruiz. 2025. Feed addi i es o me hane mi iga ion: Regula o y amewo ks and scien i ic e idence equi emen s o he au ho iza- ion o eed addi i es o mi iga e uminan me hane emissions. J. Dai y Sci. 108:395–410. h ps: / / doi .o g/ 10 .3168/ jds .2024 -25051. Uddin, M. E., J. M. T ica ico, and E. Keb eab. 2022. Impac o ni a e and 3-ni ooxyp opanol on he ca bon oo p in s o milk om ca le p oduced in con ined- eeding sys ems ac oss egions in he Uni ed S a es: A li e cycle analysis. J. Dai y Sci. 105:5074–5083. h ps: / / doi .o g/ 10 .3168/ jds .2021 -20988. an Gas elen, S., E. E. A. Bu ge s, J. Dijks a, R. de Mol, W. Muizelaa , N. Walke , and A. Bannink. 2024. Long- e m e ec s o 3-ni ooxy- p opanol on me hane emission and milk p oduc ion cha ac e is ics in Hols ein F iesian dai y cows. J. Dai y Sci. 107:5556–5573. h ps: / / doi .o g/ 10 .3168/ jds .2023 -24198. an Gas elen, S., D. Yanez-Ruiz, H. Khelil-A a, A. Blancha d, and A. Bannink. 2024. E ec o a blend o cinnamaldehyde, eugenol, and capsicum oleo esin on me hane emission and lac a ion pe o mance o Hols ein-F iesian dai y cows. J. Dai y Sci. 107:857–869. h ps: / / doi .o g/ 10 .3168/ jds .2023 -23406. an Lingen, H. J., M. Niu, E. Keb eab, S. C. Valada es Filho, J. A. Rooke, C.-A. Du hie, A. Schwa m, M. K euze , P. I. Hynd, M. Cae ano, M. Eugène, C. Ma in, M. McGee, P. O’Kiely, M. Hüne be g, T. A. McAllis e , T. T. Be chielli, J. D. Messana, N. Pei en, A. V. Cha es, E. Cha mley, N. A. Cole, K. E. Hales, S.-S. Lee, A. Be nd , C. K. Reynolds, L. A. C omp on, A.-R. Baya , D. R. Yáñez-Ruiz, Z. Yu, A. Bannink, J. Dijks a, D. P. Caspe , and A. N. H is o . 2019. P e- dic ion o en e ic me hane p oduc ion, yield and in ensi y o bee ca le using an in e con inen al da abase. Ag ic. Ecosys . En i on. 283:106575. h ps: / / doi .o g/ 10 .1016/ j .agee .2019 .106575. an Zijde eld, S. M., W. J. Ge i s, J. A. Apajalah i, J. R. Newbold, J. Dijks a, R. A. Leng, and H. B. Pe dok. 2010. Ni a e and sul a e: E - ec i e al e na i e hyd ogen sinks o mi iga ion o uminal me hane p oduc ion in sheep. J. Dai y Sci. 93:5856–5866. h ps: / / doi .o g/ 10 .3168/ jds .2010 -3281. VERRA. 2021. Ve i ied Ca bon S anda d (VCS). Accessed Oc . 20, 2024. h ps: / / e a .o g/ p og ams/ e i ied -ca bon -s anda d/ . Viba , R., C. de Klein, A. Jonke , T. an de Wee den, A. Bannink, A. R. Baya , L. C omp on, A. Du and, M. Eugene, K. Klumpp, B. Kuhla, G. Lanigan, P. Lund, M. Ramin, and F. Salaza . 2021. Challenges and oppo uni ies o cap u e die a y e ec s in on- a m g eenhouse gas emissions models o uminan sys ems. Sci. To al En i on. 769:144989. h ps: / / doi .o g/ 10 .1016/ j .sci o en .2021 .144989. ORCIDS Agus in del P ado, h ps: / / o cid .o g/ 0000 -0003 -3895 -4478 Ronaldo E. Viba , h ps: / / o cid .o g/ 0000 -0002 -0248 -3603 F anco M. Bilo o, h ps: / / o cid .o g/ 0000 -0002 -3759 -3159 Claudia Fa e in, h ps: / / o cid .o g/ 0000 -0002 -6951 -3029 Flo encia Ga cia, h ps: / / o cid .o g/ 0000 -0002 -0748 -9692 Fábio L. Hen ique, h ps: / / o cid .o g/ 0009 -0008 -6834 -2310 Fe nanda Figuei edo G anja Do ilêo Lei e, h ps: / / o cid .o g/ 0000 -0001 -9004 -6413 And e M. Mazze o, h ps: / / o cid .o g/ 0000 -0002 -1501 -0303 B adley G. Ridou , h ps: / / o cid .o g/ 0000 -0001 -7352 -0427 Da id R. Yáñez-Ruiz, h ps: / / o cid .o g/ 0000 -0003 -4397 -3905 And é Bannink h ps: / / o cid .o g/ 0000 -0001 -9916 -3202 del P ado e al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT